Project Summary/Abstract Testing individuals for infectious diseases is important for disease surveillance and for ensuring the safety of blood donations. When faced with questions on how to test as many individuals as possible and still operate within budget limits, public health of?cials are increasingly turning toward the use of group testing (pooled testing). In these applications, individual specimens (such as blood or urine) are combined to form a single pooled specimen for testing. Individuals within negative testing pools are declared negative. Individuals within positive testing pools are retested in some predetermined algorithmic manner to determine which individuals are positive and which individuals are negative. For low disease prevalence settings, this innovative testing process leads to fewer overall tests, which subsequently lowers costs, when compared to testing specimens individually. Previous research in group testing has focused largely on testing for infections, such as HIV and chlamydia, one at a time. However, motivated by the development of new technology, disease testing practices are moving towards the use of multiplex assays that detect multiple infections at once. This research proposal presents the ?rst comprehensive extensions of group testing to a multiplex assay setting. The ?rst goal is to develop new group testing strategies that allow for multiplex assays to be used in sexually transmitted disease testing and blood donation screening applications. This will allow laboratories to obtain the maximum possible cost savings through proper applications of group testing. The second goal is to develop new group testing strategies to increase the classi?cation accuracy?both with single and multiple infections?in these same applications. This will be done by performing directed con?rmatory testing after individuals are initially classi?ed as positive or negative. An overarching theme of this research is to acknowledge individual risk factors by incorporating them into the group testing process. In terms of biostatistical innovation, this research involves developing new classi?cation and Bayesian modeling procedures for correlated latent-variable data.